From 94f3df190a91898eb344b9214a57eb31792bcb1c Mon Sep 17 00:00:00 2001 From: Dylan Date: Thu, 5 Nov 2026 13:46:28 +1300 Subject: feat: Neural network opponent trained by self-play - NeuralPlayer: a MinimaxPlayer that scores positions with a small MLP (network.rs, hand-written backprop and Adam, no new dependencies) - train subcommand: self-play with TD(lambda) targets, cut at random exploration moves so sheep blunders don't bias the net towards tigers - neural subcommand: play against a trained network - evaluate now gets the side to move - Board symmetries; the search cache keys by the canonical board, which halves the depth-9 bench time with identical moves and scores Co-Authored-By: Claude Opus 5.5 --- src/game.rs | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'src/game.rs') diff --git a/src/game.rs b/src/game.rs index 11a77fd..5aef809 100644 --- a/src/game.rs +++ b/src/game.rs @@ -87,7 +87,7 @@ impl GameHandler /// Play in the terminal with `p1` as the sheep and `p2` as the tigers, until someone wins and /// the user quits. Returns the winner, or None if the user quit before the game was won. - pub fn play_game(&mut self, p1: &mut T1, p2: &mut T2) -> io::Result> + pub fn play_game(&mut self, p1: &mut dyn Player, p2: &mut dyn Player) -> io::Result> { let mut terminal = ratatui::init(); let result = execute!(io::stdout(), EnableMouseCapture) -- cgit v1.3.1